Overview
Big data/data mining is the process of analyzing enormous sets of data and extracting meaning or useful information from it using computer algorithms and/or software tools. Big data/data mining/machine learning can be used to predict behavior and future trends allowing business to make knowledge-driven decisions.
Big data/data mining tasks include data summarization, clustering, classification, prediction, and dependency analysis. Data mining relies heavily on algorithms and statistical methods to uncover patterns and create models of the data.
Big data/data mining can benefit a broad spectrum of industries helping them to increase profits by reducing costs and/or raising revenue. Students pursuing this FA could literally work in any organization that stores data and is interested in putting that data to good use.
Students interested in this FA are encouraged to consider the course suggestions listed below when completing their plan of study form:
| EE Computer Track Requirements | Suggested Options |
|---|---|
| Track | Computer |
| Depth Elective (Select One) |
ECE:5330 Graph Algorithms and Combinatorial Optimization (Same as: IGPI:5331) ECE:5320 High Performance Computer Architecture (Same as: CS:5610) ECE:5450Machine Learning(Same as: IGPI:5450) |
| Breadth Elective (Select One) |
ECE:3400 Linear System II ECE:3540 Communication Networks ECE:3600 Control Systems |
| 5000-Level ECE Elective (Select Two) |
All 5000-level depth electives listed above and ECE:5415:0001 Contemporary Topics in ECE: Radio Frequency Electronics ECE:5550 Internet of Things |
| ECE Elective (Select One) |
All breadth, depth and 5000-level ECE electives listed above |
| Technical Elective (Select Two) |
All breadth, depth and 5000-level ECE electives listed above and CS:2230 Computer Science II: Data Structures (Required) CS:5430 Machine Learning CS:4400 Database Systems CS:4420 Artificial Intelligence CS:4440 Web Mining CS:4980 Topics in Computer Science II MATH:4040 Matrix Theory STAT:4520 Bayesian Statistics STAT:4143 Intro to Statistical Methods STAT:4580 Data Visualization and Data Technologies CS:4720 Optimization Techniques (Same as: MATH:4820) IE:3149 Information Visualization IE:4172 Big Data Analytics CS:3700 Elementary Numerical Analysis (Same as: MATH:3800) |
| Additional Electives (Select one 3 s.h. & one ≥2 s.h.) |
Any of the above OR course selected in consultation with advisor. |
Advising Notes
- All computer track students satisfy the requirements for a minor in computer science.
- A minor in mathematics can be earned by including one qualifying math course in the FA plan.
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